Department of Electrical Engineering, Chiang Mai University, Chiang Mai 50200, Thailand.
Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand.
Int J Environ Res Public Health. 2023 Feb 3;20(3):2753. doi: 10.3390/ijerph20032753.
In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb-Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.
在本文中，我们提出了一种基于预测误差的自适应线性预测技术的无损心电图 (ECG) 压缩方法。该方法结合了自适应线性预测，该预测最小化了 ECG 信号预测中的预测误差，以及改进的 Golomb-Rice 编码，将预测误差编码为二进制码作为压缩数据。我们使用了 PTB 诊断 ECG 数据库、欧洲 ST-T 数据库和麻省理工学院-贝思以色列医院心律失常数据库进行评估，分别实现了单导联 ECG 信号的平均压缩比为 3.16、3.75 和 3.52，尽管每个数据库中的信号采集设置不同。由于预测顺序对于这个特定问题非常关键，我们还通过自相关方法从三个数据库中确定预测系数，研究了通常用于 ECG 压缩的流行线性预测系数的有效性。研究结果与先前的工作一致，即二阶线性预测适用于 ECG 压缩应用。
